Validating a local Arterial Input Function method for improved perfusion quantification in stroke - PubMed (original) (raw)

Validating a local Arterial Input Function method for improved perfusion quantification in stroke

Lisa Willats et al. J Cereb Blood Flow Metab. 2011 Nov.

Abstract

In bolus-tracking perfusion magnetic resonance imaging (MRI), temporal dispersion of the contrast bolus due to stenosis or collateral supply presents a significant problem for accurate perfusion quantification in stroke. One means to reduce the associated perfusion errors is to deconvolve the bolus concentration time-course data with local Arterial Input Functions (AIFs) measured close to the capillary bed and downstream of the arterial abnormalities causing dispersion. Because the MRI voxel resolution precludes direct local AIF measurements, they must be extrapolated from the surrounding data. To date, there have been no published studies directly validating these local AIFs. We assess the effectiveness of local AIFs in reducing dispersion-induced perfusion error by measuring the residual dispersion remaining in the local AIF deconvolved perfusion maps. Two approaches to locating the local AIF voxels are assessed and compared with a global AIF deconvolution across 19 bolus-tracking data sets from patients with stroke. The local AIF methods reduced dispersion in the majority of data sets, suggesting more accurate perfusion quantification. Importantly, the validation inherently identifies potential areas for perfusion underestimation. This is valuable information for the identification of at-risk tissue and management of stroke patients.

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Figures

Figure 1

Figure 1

Flow chart outlining the steps involved in forming the various parameter maps, and their relationship to each other. The chart is divided into four quadrants. The top two quadrants (A) contain processes for calculating perfusion maps using global AIF (GAIF, left) and local AIFs (LAIF1 and LAIF2, right). The bottom two quadrants (B) show the process for obtaining the dispersion maps from GAIF (left) and from LAIF1 and LAIF2 (right). Note that in (B) the Tissue CTC Data, GAIF, LAIF1, and LAIF2 are taken from section (A). The subscripts G, L1, and L2 refer to the maps obtained using GAIF, LAIF1, and LAIF2, respectively. For further clarity, the GAIF steps are in black, the LAIF1 steps are in blue, and the LAIF2 steps are in red. AIF, Arterial Input Function; CTC, concentration time-course; GAIF, global AIF; oSVD, delay-insensitive deconvolution; Tmax, time-to-maximum value of GAIF oSVD residue function; _FM_R, First Moment of GAIF residue function; CBF, cerebral blood flow; MTT, mean transit time; CBV, cerebral blood volume; _FM_adj, adjusted First Moment (equation (1)); _FM_C, FM of the CTC; LAIF, local Arterial Input Function; owMLEM, maximum-likelihood expectation-maximization deconvolution regularized with an oscillation index and wavelet thresholding; RTM, time to rise-to-maximum value the owMLEM residue function.

Figure 2

Figure 2

Example simulation results at TR=2 seconds and signal-to-noise ratio=50 showing the BAT estimate (_FM_C=blue dashed-dot line, _FM_adj=red solid line) as a function of (A) simulated MTT (delay=0 second, dispersion=0 second), (B) simulated dispersion (MTT=4 seconds, delay=0 second), and (C) simulated delay (MTT=4 seconds, dispersion=0 second). Error bars are ±1s.d. BAT, bolus arrival time; _FM_C, First Moment of the concentration time-course, _FM_adj, adjusted First Moment (equation (1)); MTT, mean transit time; TR, repetition time.

Figure 3

Figure 3

Graph showing the %IH with abnormality using GAIF (dotted bars) and LAIF2 (hashed bars). The top graph (A) shows the dispersion (RTM≥TR) and the bottom graph (B) shows the hypoperfusion (MTT>Thld) for each data set A–S. GAIF, global Arterial Input Function; LAIF2, local Arterial Input Function (new method); RTM, time to rise-to-maximum value of the owMLEM residue function; MTT, mean transit time; Thld=MTTcontraGM+1.78 seconds (see text); TR, repetition time.

Figure 4

Figure 4

Abnormal perfusion and dispersion areas from example slices in data sets A (i), F (ii), K (iii), and S (iv). In each subfigure (i–iv), the diffusion weighted image slice most closely corresponding to the perfusion slice is shown to the right. The remaining four maps are MTT, with areas of MTT>Thld overlaid in yellow (top) and RTM≥TR overlaid in blue (bottom); the left-most maps are the GAIF analysis and the central maps are the LAIF2 analysis. The crosses marking the LAIF2 perfusion maps of (i) and (iii) are the locations from which the LAIF2 time-courses in Figure 5 are taken. GAIF, global Arterial Input Function; LAIF2, local Arterial Input Function (new method); RTM, time to rise-to-maximum value of the owMLEM residue function; MTT, mean transit time; Thld=MTTcontraGM+1.78 seconds (see text).

Figure 5

Figure 5

Example LAIF2 time-courses (red) from patients A and K (panels A and B, respectively) together with the GAIF time-course (black dashed). Both are normalized to the first passage. The locations of the local AIF voxels are shown by the crosses in Figure 4. AIF, Arterial Input Function; GAIF, global AIF; LAIF2, local AIF (new method).

Figure 6

Figure 6

Example of how local AIF methodology may delineate final infarct volume more closely than the standard GAIF method. The left and central maps are MTT with areas of MTT>Thld overlaid in yellow for (i) GAIF and (ii) LAIF2 at acute scan time of 3 hours (data set O, Table 1). The right hand image (iii) is the follow-up 108 day diffusion scan. AIF, Arterial Input Function; GAIF, global AIF; LAIF2, local AIF (new method); MTT, mean transit time; Thld=MTTcontraGM+1.78 seconds (see text).

References

    1. Albers GW, Thijs VN, Wechsler L, Kemp S, Schlaug G, Skalabrin E, Bammer R, Kakuda W, Lansberg MG, Shuaib A, Coplin W, Hamilton S, Moseley M, Marks MP. Magnetic resonance imaging profiles predict clinical response to early reperfusion: the diffusion and perfusion imaging evaluation for understanding stroke evolution (DEFUSE) study. Ann Neurol. 2006;60:508–517. -PubMed
    1. Alsop D, Wedmid A, Schlaug G.2002Defining a local arterial input function for perfusion quantification with bolus contrast MRI10th Annual Meeting of ISMRM, Honolulu, HI, USA, p. 659
    1. Calamante F, Christensen S, Desmond PM, Ostergaard L, Davis SM, Connelly A. The physiological significance of the time-to-maximum (Tmax) parameter in perfusion MRI. Stroke. 2010;41:1169–1174. -PubMed
    1. Calamante F, Gadian DG, Connelly A. Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition. Magn Reson Med. 2000;44:466–473. -PubMed
    1. Calamante F, Morup M, Hansen LK. Defining a local arterial input function for perfusion MRI using independent component analysis. Magn Reson Med. 2004;52:789–797. -PubMed

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